There are a few works [1]}, [2]}, [3]}, [4]}, [5]}, [6]} studying how to select relevant subsets from the pre-training dataset to improve the performance when transferred to target datasets. [4]} use a feature extractor trained on the JFT300M [8]} to select the most similar source categories to the target categories in a greedy way. For each image from the target domain, Ge  [3]} search a certain number of images with similar low-level characteristics from the source domain. Shuvam  [1]} train the feature extractor on the target data and individually select source images that are close to cluster centers in the target domain. Yan  [6]} propose a Neural Data Server (NDS) to train expert models on many subsets of the pre-training dataset. Source images used to train an expert with a good target task performance are assigned high importance scores. It is noted that conditional transfer learning has mainly been studied on close-set tasks such as image classification, fine-grained recognition, object detection, etc. Therefore, these methods may be not suitable for the open-set ReID task.
